Is better to spend parameters on weights or bias?
First of all, your estimates are a bit off. If you have 300 neurons, you won't have just 2 weights per neuron, but much more, assuming full connectivity

Bias isn't just an extra parameter to fit, it is an important adjustable parameter that sets the offset of the separating hyperplane represented by each neuron. Think of the simple equation $ax+b$, there's no way to shift the line unless you use the $b$ (bias) part.

This would be especially important for small number of nodes and classification tasks (think perceptrons etc)